CN109404285A - The algorithm enhancing self-adaptive band-pass filter method that leapfrogs is shuffled in a kind of improvement of screw compressor fault diagnosis - Google Patents
The algorithm enhancing self-adaptive band-pass filter method that leapfrogs is shuffled in a kind of improvement of screw compressor fault diagnosis Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
- F04C—ROTARY-PISTON, OR OSCILLATING-PISTON, POSITIVE-DISPLACEMENT MACHINES FOR LIQUIDS; ROTARY-PISTON, OR OSCILLATING-PISTON, POSITIVE-DISPLACEMENT PUMPS
- F04C28/00—Control of, monitoring of, or safety arrangements for, pumps or pumping installations specially adapted for elastic fluids
- F04C28/28—Safety arrangements; Monitoring
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
- F04C—ROTARY-PISTON, OR OSCILLATING-PISTON, POSITIVE-DISPLACEMENT MACHINES FOR LIQUIDS; ROTARY-PISTON, OR OSCILLATING-PISTON, POSITIVE-DISPLACEMENT PUMPS
- F04C2270/00—Control; Monitoring or safety arrangements
- F04C2270/80—Diagnostics
Abstract
The algorithm enhancing self-adaptive band-pass filter method that leapfrogs is shuffled in the improvement that the present invention discloses a kind of screw compressor fault diagnosis, first, collected vibration signal is subjected to EEMD processing, calculates the related kurtosis value of each IMF component, related kurtosis value maximum is picked up and secondary big component carries out signal reconstruction；Secondly reconstruction signal handled based on the self-adaptive band-pass filter for improving SFLA algorithm, precisely interception is rich in the high frequency band signal of fault message；Filtered signal is finally subjected to Hilbert envelope demodulation analysis, spectrum analysis is carried out to demodulated signal, last diagnostic goes out compressor fault.On the one hand IMF component is carried out using related kurtosis value and be reconstructed into new signal, both remained most fault messages and in turn avoided the influence of noise and pseudo- component to feature extraction；On the other hand, the algorithm enhancing adaptive bandpass filter that leapfrogs is shuffled based on improved, self-adaptive band-pass filter is carried out to reconstruction signal, the centre frequency and bandwidth that optimize bandpass filtering improve the precision of fault diagnosis.
Description
Technical field
The present invention relates to the improvement of mechanical fault diagnosis field, in particular to screw compressor fault diagnosis to shuffle
The algorithm that leapfrogs enhances self-adaptive band-pass filter method.
Background technique
Screw compressor is compared to work as a kind of dual-axis rotation formula compressor for changing principle by volume and working
Plug compressor and centrifugal compressor, screw compressor simple, reliable operation and systems such as volumetric efficiency is good with its structure
Column particular advantages, the usage amount in air force, refrigeration air-conditioner and the various process flows of petrochemical industry are most.As large size
The part of core will lead to occur if cannot judge the operating status of compressor accurately and in time the most in pressure system
The probability of catastrophic failure increases, and influences the normal operation and service life of unit, or even cause more huge economic loss.Therefore,
Fault diagnosis is carried out to helical-lobe compressor, to the normal operation for guaranteeing production, the economic benefit for improving enterprise has important meaning
Justice.
However, the gap between compressor part cooperation is minimum, movement is very fast, only passes through the display of part instrument and practical warp
The damage for carrying out judgement part is tested, accuracy and actual effect are very poor.In addition, the fault features of compressor are very faint, it is real
Under the operating condition of border, the original quality bias quasi-periodic pulse excitation of damage fault, rotor can excite each rank of compressor assembly total
Vibration frequency, and signal high band is mingled with much noise, and the pulse repetition for carrying fault message is caused easily to be flooded by noise.For
By the vibration of compressor signal of property free period pulse severe jamming, if only carrying out single signal processing method, effect is demodulated
Difference, noise reduction effect is bad, and fault characteristic information extraction effect is often not ideal enough.Therefore, it for Compressor Fault Diagnosis, needs
Urgently solve the problems, such as to be exactly reliable compressor fault feature extracting method.
Summary of the invention
The purpose of the invention is to overcome shortcoming and defect of the existing technology, and provide a kind of screw compressor
The algorithm enhancing self-adaptive band-pass filter method that leapfrogs is shuffled in the improvement of fault diagnosis.This method carries out adaptive band to reconstruction signal
Pass filter, the centre frequency and bandwidth that optimize bandpass filtering improve the precision of fault diagnosis.
To achieve the above object, the technical scheme is that including:
S1, the original vibration signal of collected screw compressor is carried out to set empirical mode decomposition processing, picked up
Related kurtosis value maximum and secondary big IMF component carry out signal reconstruction, remain most fault characteristic informations and avoid strong
The interference of noise；
S2, the self-adaptive band-pass filter for secondly shuffled based on improvement the algorithm that leapfrogs to reconstruction signal are handled, in optimization
Frequency of heart and bandwidth, further precisely interception is rich in the high frequency band signal of fault message, and resonance and demodulation is out of order signal envelope
Spectrum；
S3, the analysis that fault characteristic frequency is finally carried out to fault-signal envelope spectrum, are diagnosed to be the event of screw compressor
Barrier.
Further setting is in the step S1 specifically:
For the original vibration signal x (t) of screw compressor, gather the specific steps of empirical mode decomposition processing such as
Under:
(1) Gaussian sequence is added to original signal x (t), and to adding the signal x (t) to be analyzed after making an uproar to return
One change processing；
(2) signal is subjected to empirical mode decomposition, obtains k IMF component cj(t) and surplus r (t) (j=
1...k)。
(3) Gaussian sequence that amplitude is identical but normal distribution is different is added every time, repeats step (1), (2) n times,
I.e.
In formula, xiIt (t) is the signal after i addition white Gaussian noise, cijFor Empirical Mode after i-th addition white Gaussian noise
J-th of IMF component that state is decomposed, (j=1...k)；
(4) it calculates to decompose every time and obtains the mean value of corresponding IMF component, inhibit or completely eliminate that white noise is repeatedly added to true
The influence of real IMF component, it is final to obtain set empirical mode decomposition treated that IMF component is
In formula, N is the set number of empirical mode decomposition, cij(t) it is j-th obtained of i-th empirical mode decomposition
IMF component；
(5) the related kurtosis for solving each IMF component chooses related kurtosis value maximum and secondary big IMF component reconstruction signal,
The mathematic(al) representation of the correlation kurtosis are as follows:
In formula, xnFor vibration signal, T is the period of impact signal interested, and N is sampling length, and M is the period of offset
Number.
Further setting is that improvement in the step S2 shuffles the algorithm that leapfrogs specific step is as follows:
(1) initialization population parameter, including frog Population Size N, frog individual Pi=(xi,1,xi,2,...,xi,d)xi,j
∈[aj,bj], subcluster number m, subgroup the number of iterations n and subgroup inner iteration times Ne, allow mobile maximum step-length Smax, establish suitable
Response function determines that optimized individual fitness is optimization aim, establishes and stop design conditions；
(2) the galloping long update formula S of frog-jumping in the algorithm that leapfrogs is shuffled in originali=rand () × (Pb-Pw), Si∈[Smin,
Smax], rand () is the random number between 0 to 1, PbTo organize interior optimal frog, PwOn the basis of organizing interior worst frog,
Formula is updated to S 'i=qSi+rand()×(Pb-Pw), S 'i∈[Smin,Smax], wherein q takes the random number between 0 to 1,
PwMore new formula is P 'w=Pw+S′i, P 'w∈[Pmin,Pmax].If P ' after updatingwBetter than Pw, then replace Pw, otherwise by public affairs
Formula S 'i=rand () × (Pg-Pw)S′i∈[Smin,Smax] calculate frog step-length；
(3) based on determining N number of frog individual Pi, utilize formula (1)
aj(t)=min (xj(t),bj(t)=max (xj(t)) t is current iteration number
I=1,2 ..., N；J=1,2 ..., d；K=rand (0,1)
N number of reversed individual is generated, 2N individual is arranged by fitness ascending order, selects top n individual composition a new generation kind
Group；
(4) population is divided into m subgroup；N is carried out in subgroup using improved more new formula in step (2)eSecondary iteration；
(5) each subgroup is re-mixed and generates new population；
(6) corresponding N number of reversed individual is generated based on new population at individual；
(7) 2N individual is arranged by fitness descending, selects top n individual and forms population of new generation；
(8) fitness for calculating individual, judges whether the termination condition of algorithm meets, and algorithm terminates if meeting, otherwise
It goes to step (4), the number of iterations n.
Further setting is that the algorithm that leapfrogs that shuffled based on improvement in the step S2 carries out self-adaptive band-pass filter
Step are as follows:
(1) variation range of tentatively specified bandpass filtering centre frequency and bandwidth；
(2) two-dimensional frog individual is determinedWherein fc: centre frequency fb: bandwidth), initialization population
Parameter establishes fitness function, determines that optimized individual fitness is optimization object function, establishes and stop design conditions, with optimization
Target function value continuous five times be less than certain value when stop calculate；
(3) parameter optimization for carrying out self-adaptive band-pass filter to reconstruction signal based on improved SFLA algorithm, optimizing are carried out
Obtain centre frequency and bandwidth corresponding to optimal adaptation degree frog individual；
(4) optimal resonance and demodulation envelope spectrum corresponding to optimal centre frequency and bandwidth is obtained.
Innovation Mechanism of the invention is:
Gathering empirical mode decomposition (Ensemble Empirical Mode Decomposition, EEMD) is substantially
A kind of noise auxiliary signal analysis method utilizes Gauss repeatedly to the white Gaussian noise for adding certain amplitude in signal to be processed
White noise has the statistical property of frequency-flat distribution, is automatically mapped to the signal of different scale on reference scale appropriate,
So as to avoid the error of fitting of envelope, modal overlap is efficiently solved the problems, such as.To repeatedly decompose obtained IMF component into
Row population mean, to achieve the effect that inhibit or completely eliminate influence of noise.
Improve SFLA algorithm be on the basis of SFLA algorithm, to joined in the improvement for shuffling the algorithm that leapfrogs memory function and
Backward learning (OBL) function, memory function refer to: when individual updates every time, step-length when worst frog individual updates is remembered,
And it is introduced into individual update next time；Backward learning function refers to: adding respectively in initialization of population and evolutionary process
Enter reverse operating, OBL is applied in the iteration each time of evolutionary process, so that individual is centered around by the optimal geometry being deconstructed into
Immediate vicinity activity, improves convergence speed of the algorithm and optimizing ability.
The effect of bandpass filter is to intercept the signal for being rich in fault message, and whether centre frequency and bandwidth are reasonable
By directly determine by the fault message in filtered signal number (i.e. signal-to-noise ratio size).Center frequency value and bandwidth Design
It is more reasonable, higher by the Signal-to-Noise of filtering, the peak value in envelope spectrum at fault characteristic frequency will highlight.
The present invention is used for self-adaptive band-pass filter for SFLA algorithm is improved, and can precisely determine the optimal center frequency of filter effect
Rate and bandwidth, effectively extract fault characteristic frequency.In order to solve the problems, such as that compressor fault characteristic information is difficult to extract, propose
A kind of screw compressor method for diagnosing faults combined based on EEMD with improvement hill-climbing algorithm enhancing self-adaptive band-pass filter,
Related this respect research, there is no report at present.
On the one hand the method for the present invention carries out IMF component using related kurtosis value and is reconstructed into new signal, both remained at most
Fault message in turn avoid the influence of noise and pseudo- component to feature extraction；On the other hand, the calculation that leapfrogs is shuffled based on improved
Method enhance adaptive bandpass filter, to reconstruction signal carry out self-adaptive band-pass filter, optimize bandpass filtering centre frequency and
The precision of bandwidth raising fault diagnosis.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention, for those of ordinary skill in the art, without any creative labor, according to
These attached drawings obtain other attached drawings and still fall within scope of the invention.
Fig. 1 the principle of the present invention flow chart；
The screw compressor vibration signal processing flow chart of Fig. 2 embodiment of the present invention；
The original vibration signal figure of Fig. 3 embodiment of the present invention screw compressor collected, Fig. 3 (a) are time-domain diagram；Figure
3 (b) be frequency domain figure；
The time domain waveform of preceding 4 IMF components of Fig. 4 embodiment of the present invention；
Each IMF component correlation kurtosis value distribution curve of Fig. 5；
Fig. 6 reconstruction signal time domain waveform；
Fig. 7 reconstruction signal frequency-domain waveform figure；
The optimal resonance and demodulation envelope spectrogram of Fig. 8 reconstruction signal.
Specific embodiment
To make the object, technical solutions and advantages of the present invention clearer, the present invention is made into one below in conjunction with attached drawing
Step ground detailed description.
Noun explanation:
Part technical term is expressed using English abbreviation in the present embodiment:
Gather empirical mode decomposition (Ensemble Empirical Mode Decomposition, EEMD)；
The algorithm that leapfrogs (Shuffled Frog Leaping Algorithm, SFLA) is shuffled in improvement；
Bandpass filtering (Bandpass Filter, BF)；
Basic friction angle component (IMF).
As shown in Figure 1, including following steps for technical solution of the present invention:
S1, the original vibration signal of collected screw compressor is carried out to set empirical mode decomposition processing, picked up
Related kurtosis value maximum and secondary big IMF component carry out signal reconstruction, remain most fault characteristic informations and avoid strong
The interference of noise；
S2, the self-adaptive band-pass filter for secondly shuffled based on improvement the algorithm that leapfrogs to reconstruction signal are handled, in optimization
Frequency of heart and bandwidth, further precisely interception is rich in the high frequency band signal of fault message, and resonance and demodulation is out of order signal envelope
Spectrum；
S3, the analysis that fault characteristic frequency is finally carried out to fault-signal envelope spectrum, are diagnosed to be the event of screw compressor
Barrier.
The present embodiment the following steps are included:
1, EEMD handles original vibration signal and carries out signal reconstruction based on related kurtosis value.
EEMD is proposed a kind of new to effectively inhibit modal overlap problem present in EMD method on the basis of EMD
Data processing method --- set empirical mode decomposition.Substantially be a kind of noise auxiliary signal analysis method, repeatedly to
The white Gaussian noise of certain amplitude is added in processing signal, because white Gaussian noise has the statistical property of frequency-flat distribution,
So the signal of different scale can be made to be automatically mapped on reference scale appropriate using this characteristic, so as to avoid envelope
The error of fitting of line, efficiently solves the problems, such as modal overlap.
For the original vibration signal x (t) of compressor, specific step is as follows for EEMD processing:
(1) Gaussian sequence is added to original signal x (t), and to adding the signal x (t) to be analyzed after making an uproar to return
One change processing.
(2) signal is subjected to EMD decomposition, obtains k IMF component cj(t) and a surplus r (t) (j=1...k).
(3) Gaussian sequence that amplitude is identical but normal distribution is different is added every time, repeats step (1), (2) n times,
I.e.
In formula, xiIt (t) is the signal after i addition white Gaussian noise, cijEMD divides after white Gaussian noise is added for i-th
J-th of IMF component that solution obtains, (j=1...k).
(4) it calculates to decompose every time and obtains the mean value of corresponding IMF, inhibit or completely eliminate that white noise is repeatedly added to true
The influence of IMF component, the IMF after final acquisition EEMD is decomposed are
In formula, N is the set number of EMD, cijIt (t) is i-th EMD j-th of IMF obtained.
(5) the related kurtosis for solving each IMF component chooses related kurtosis value maximum and secondary big IMF component reconstruction signal.
Related kurtosis has both the feature of kurtosis and correlation function, can effectively reflect specific period impact signal in signal
Intensity.Related kurtosis value is bigger, shows more comprising impact signal interested in signal.The mathematic(al) representation of related kurtosis are as follows:
In formula, xnFor vibration signal, T is the period of impact signal interested, and N is sampling length, and M is the period of offset
Number.
2, reconstruction signal is handled based on the self-adaptive band-pass filter for improving SFLA algorithm.
Aiming at the problem that determining self-adaptive band-pass filter centre frequency, the present invention is proposed using the increase note in algorithm improvement
Function and backward learning (OBL) function are recalled to improve the improvement SFLA algorithm of convergence rate and optimizing ability, and construction is based on improvement
The adaptive bandpass filter of SFLA algorithm searches best corresponding complete of demodulation by filter effect in centre frequency variation range
Office's optimal solution.
Based on improved SFLA algorithm, specific step is as follows:
(1) initialization population parameter, including frog Population Size N, frog individual Pi=(xi,1,xi,2,...,xi,d)xi,j
∈[aj,bj], subcluster number m, subgroup the number of iterations n and subgroup inner iteration times Ne, allow mobile maximum step-length Smax, establish suitable
Response function determines that optimized individual fitness is optimization aim, establishes and stop design conditions；
(2) the galloping long update formula S of frog-jumping in the algorithm that leapfrogs is shuffled in originali=rand () × (Pb-Pw), Si∈[Smin,
Smax], rand () is the random number between 0 to 1, PbTo organize interior optimal frog, PwOn the basis of organizing interior worst frog,
Formula is updated to Si'=qSi+rand()×(Pb-Pw), S 'i∈[Smin,Smax], wherein q takes the random number between 0 to 1,
PwMore new formula is P 'w=Pw+S′i, P 'w∈[Pmin,Pmax].If P ' after updatingwBetter than Pw, then replace Pw, otherwise by public affairs
Formula S 'i=rand () × (Pg-Pw)S′i∈[Smin,Smax] calculate frog step-length；
(3) based on determining N number of frog individual Pi, utilize formula (1)
aj(t)=min (xj(t),bj(t)=max (xj(t)) t is current iteration number
I=1,2 ..., N；J=1,2 ..., d；K=rand (0,1)
N number of reversed individual is generated, 2N individual is arranged by fitness ascending order, selects top n individual composition a new generation kind
Group.
(4) population is divided into m subgroup；N is carried out in subgroup using more new formula improved in (2)eSecondary iteration.
(5) each subgroup is re-mixed and generates new population.
(6) corresponding N number of reversed individual is generated based on new population at individual.
(7) 2N individual is arranged by fitness descending, selects top n individual and forms population of new generation.
(8) fitness for calculating individual, judges whether the termination condition of algorithm meets, and algorithm terminates if meeting, otherwise
It goes to step (4), the number of iterations n.
Main fault characteristic frequency is respectively as follows: bearing inner race failure-frequency f in compressor assemblyi, outer ring failure-frequency
fo, rolling element failure-frequency fb, screw rotor meshing frequency fr.The approximate calculation of fault characteristic frequency:
fr=nfα (5)
In formula, z is rolling element number, and d rolling element diameter (mm), D is bearing pitch diameter (mm), and α is pressure angle, fαTo turn frequency
(Hz), n is the thread number of screw rotor.
Enable fmax=max (fi,fo,fb,fr), fmin=max (fi,fo,fb,fr), the establishment process of individual fitness function
Are as follows:
(1) the corresponding centre frequency of the optimum individual obtained according to each iteration and bandwidth carry out resonance solution to reconstruction signal
It adjusts, obtains corresponding envelope spectral curve；
(2) the maximum value y of envelope range value is searched in all frequency ranges of envelope spectrummax；
(3) in [fmin-ε,fmax+ ε] the interior search spectrum peak y of range1maxAnd its frequency values f at place1max；ε value can basis
Empirical equation determines:
(4) y is comparedmaxAnd y1maxIf y1max< ymax, reject y1maxValue, otherwise retains；
(5) respectively in [2f1max-2ε,2f1max+ 2 ε] and [3f1max-3ε,3f1max+ 3 ε] search peak y in range2maxWith
y3maxAnd its frequency values f at place2maxAnd f3max；
(6) fitness function of individual is calculated according to formula (6)；
Mean is the average value for taking two numerical value in bracket in formula, with 3 rank effective peak preceding in Fault-Sensitive frequency range it
Between relative difference and peak value size as optimization aim, the position of Lai Youhua centre frequency and bandwidth.Wherein, relative difference is determined
Determine fault signature obvious degree, peak value size determines whether signal output is reasonable.If gained resonance and demodulation envelope spectrum is ideal shape
State, y1maxFor the fundamental frequency amplitude of a certain fault characteristic frequency of compressor, y2maxFor 2 frequency multiplication amplitudes, y3maxFor 3 frequency multiplication amplitudes.
The step of carrying out self-adaptive band-pass filter the present invention is based on improved SFLA algorithm are as follows:
(1) variation range of tentatively specified bandpass filtering centre frequency and bandwidth；
(2) two-dimensional frog individual is determined(fc: centre frequency fb: bandwidth) initialization population parameter,
Fitness function is established, determines that optimized individual fitness is optimization object function, establishes and stop design conditions, with optimization aim letter
Numerical value continuous five times be less than certain value when stop calculate；
(3) parameter optimization for carrying out self-adaptive band-pass filter to reconstruction signal based on improved SFLA algorithm, optimizing are carried out
Obtain centre frequency and bandwidth corresponding to optimal adaptation degree frog individual；
(4) optimal resonance and demodulation envelope spectrum corresponding to optimal centre frequency and bandwidth is obtained.
3, spectrum analysis resonance and demodulation envelope spectrum diagnoses the specific failure of compressor.
Obtained optimal envelope spectrogram is demodulated according to adaptive resonance, finds peak value and its corresponding frequency in spectrogram
Value is compared with the resulting theoretical value of compressor fault characteristic frequency approximate calculation, and last diagnostic goes out compressor fault and determines compression
The abort situation of machine.
The compression that the algorithm enhancing self-adaptive band-pass filter that leapfrogs combines is shuffled with improvement based on set empirical mode decomposition
Machine vibration signal processing flow is as shown in Figure 2.
Case study on implementation 1: yin-yang rotor engages bad diagnosis
Be used to diagnose one for method proposed by the present invention causes yin-yang rotor engagement undesirable because male rotor lead is overproof
Twin-screw compressor, failure show as the trace of phase mutual friction between the obvious rotor of rotor surface；
The compressor male rotor thread number be 4, male rotor speed be 29Hz, according to the compressor known features,
The approximate calculation of bearing of compressor known features, formula (2)~(5) compressor fault characteristic frequency, can be obtained the compressor in table 1
Fault characteristic frequency.
1 compressor fault characteristic frequency of table
1, EEMD handles the vibration signal of compressor and carries out signal reconstruction based on related kurtosis value.
1) vibration signal of compressor, such as Fig. 3 are acquired.
2) original vibration signal for intercepting 0~0.7s carries out EEMD decomposition, obtains 14 IMF components and 1 surplus, and preceding 4
A IMF component time domain waveform is as shown in Figure 4.
3) according to formula (1), the impact signal period interested is setTake offset period M=
5, the related kurtosis value of each IMF component is calculated separately, related kurtosis value distribution curve is as shown in Figure 5.
4) choose the correlation maximum IMF2 of kurtosis value and time big IMF1 and carry out signal reconstruction, reconstruction signal time domain waveform with
Frequency-domain waveform is as shown in Fig. 6 Fig. 7.
2, reconstruction signal is handled based on the self-adaptive band-pass filter for improving SFLA algorithm.
According to the frequency-domain waveform of reconstruction signal, based on the self-adaptive band-pass filter optimization process for improving SFLA algorithm are as follows:
(1) variation range of tentatively specified bandpass filtering centre frequency and bandwidth is [1000,10000], [100,2000]
Hz；
(2) initialization population parameter, setting frog individual amount are i=100, are divided into m=10 subgroup, each subgroup
In have n=10 frog individual, determine frog solution space for two dimension, i.e., two-dimensional frog individual(fc: in
Frequency of heart fb: bandwidth), the step-length that leapfrogs limits range as [- 100,100] Hz, subgroup inner iteration times Ne=20, subgroup iteration time
Number n=50, establishes fitness function f, determines that optimized individual fitness is optimization object function, continuous with optimization object function value
Five times less than 1 × 10-5To stop design conditions；
(3) parameter optimization for carrying out self-adaptive band-pass filter to reconstruction signal based on improved SFLA algorithm, optimizing are carried out
Obtain centre frequency f corresponding to optimal adaptation degree frog individualc=7800Hz and bandwidth fb=500Hz；
(4) optimal resonance and demodulation envelope spectrum corresponding to optimal centre frequency and bandwidth is obtained.
Resonance and demodulation processing is carried out to reconstruction signal using based on the self-adaptive band-pass filter for improving SFLA algorithm, it is resulting
Optimal resonance and demodulation envelope spectrum is as shown in Figure 8.
3, spectrum analysis resonance and demodulation envelope spectrum diagnoses the specific failure of compressor.
Obtained optimal envelope spectrogram is demodulated according to adaptive resonance, finds peak value and its corresponding frequency in spectrogram
Value, as shown in Figure 8.Crest frequency is respectively 116Hz, 231.6Hz, 348Hz, 461.1Hz, 576.5Hz, 690.3Hz, by gained
Peak value and the resulting theoretical value of 1 compressor fault characteristic frequency approximate calculation of table compare, it is found that these characteristic frequencies are all yin-yang
It is bad can be diagnosed to be the engagement that the specific failure of compressor is yin-yang rotor for the meshing frequency and frequency multiplication of rotor.
Those of ordinary skill in the art will appreciate that implement the method for the above embodiments be can be with
Relevant hardware is instructed to complete by program, the program can be stored in a computer readable storage medium,
The storage medium, such as ROM/RAM, disk, CD.
The above disclosure is only the preferred embodiments of the present invention, cannot limit the right model of the present invention with this certainly
It encloses, therefore equivalent changes made in accordance with the claims of the present invention, is still within the scope of the present invention.
Claims (4)
1. the algorithm enhancing self-adaptive band-pass filter method that leapfrogs, feature are shuffled in a kind of improvement of screw compressor fault diagnosis
Be include:
S1, the original vibration signal of collected screw compressor is carried out to set empirical mode decomposition processing, picked up related
Kurtosis value maximum and secondary big IMF component carry out signal reconstruction, remain most fault characteristic informations and avoid very noisy
Interference；
S2, secondly reconstruction signal is carried out based on the self-adaptive band-pass filter processing for shuffling the algorithm that leapfrogs is improved, optimization center is frequently
Rate and bandwidth, further precisely interception be rich in fault message high frequency band signal, and resonance and demodulation be out of order signal envelope spectrum；
S3, the analysis that fault characteristic frequency is finally carried out to fault-signal envelope spectrum, are diagnosed to be the failure of screw compressor.
2. it is adaptive that the algorithm enhancing that leapfrogs is shuffled in a kind of improvement of screw compressor fault diagnosis according to claim 1
Band-pass filtering method, it is characterised in that: in the step S1 specifically:
For the original vibration signal x (t) of screw compressor, gathering empirical mode decomposition processing, specific step is as follows:
(1) Gaussian sequence is added to original signal x (t), and to adding the signal x (t) to be analyzed after making an uproar to be normalized
Processing；
(2) signal is subjected to empirical mode decomposition, obtains k IMF component cj(t) and a surplus r (t) (j=1...k)；
(3) Gaussian sequence that amplitude is identical but normal distribution is different is added every time, repeats step (1), (2) n times, i.e.,
In formula, xiIt (t) is the signal after i addition white Gaussian noise, cijEmpirical modal divides after white Gaussian noise is added for i-th
J-th of IMF component that solution obtains, (j=1...k)；
(4) it calculates to decompose every time and obtains the mean value of corresponding IMF component, inhibit or completely eliminate that white noise is repeatedly added to true
The influence of IMF component, it is final to obtain set empirical mode decomposition treated that IMF component is
In formula, N is the set number of empirical mode decomposition, cijIt (t) is i-th empirical mode decomposition j-th of IMF obtained points
Amount；
(5) the related kurtosis for solving each IMF component chooses related kurtosis value maximum and secondary big IMF component reconstruction signal, the phase
Close the mathematic(al) representation of kurtosis are as follows:
In formula, xnFor vibration signal, T is the period of impact signal interested, and N is sampling length, and M is the number of cycles of offset.
3. it is adaptive that the algorithm enhancing that leapfrogs is shuffled in a kind of improvement of screw compressor fault diagnosis according to claim 2
Band-pass filtering method, it is characterised in that: the algorithm that leapfrogs is shuffled in the improvement in the step S2, and specific step is as follows:
(1) initialization population parameter, including frog Population Size N, frog individual Pi=(xi,1,xi,2,...,xi,d)xi,j∈[aj,
bj], subcluster number m, subgroup the number of iterations n and subgroup inner iteration times Ne, allow mobile maximum step-length Smax, establish fitness letter
Number determines that optimized individual fitness is optimization aim, establishes and stop design conditions；
(2) the galloping long update formula S of frog-jumping in the algorithm that leapfrogs is shuffled in originali=rand () × (Pb-Pw), Si∈[Smin,
Smax], rand () is the random number between 0 to 1, PbTo organize interior optimal frog, PwOn the basis of organizing interior worst frog,
Formula is updated to S 'i=qSi+rand()×(Pb-Pw), S 'i∈[Smin,Smax], wherein q takes the random number between 0 to 1,
PwMore new formula is P 'w=Pw+S′i, P 'w∈[Pmin,Pmax], if P ' after updatingwBetter than Pw, then replace Pw, otherwise by public affairs
Formula S 'i=rand () × (Pg-Pw)S′i∈[Smin,Smax] calculate frog step-length；
(3) based on determining N number of frog individual Pi, utilize formula (1)
aj(t)=min (xj(t),bj(t)=max (xj(t)) t is current iteration number
I=1,2 ..., N；J=1,2 ..., d；K=rand (0,1)
N number of reversed individual is generated, 2N individual is arranged by fitness ascending order, top n individual is selected and forms population of new generation；
(4) population is divided into m subgroup；N is carried out in subgroup using improved more new formula in step (2)eSecondary iteration；
(5) each subgroup is re-mixed and generates new population；
(6) corresponding N number of reversed individual is generated based on new population at individual；
(7) 2N individual is arranged by fitness descending, selects top n individual and forms population of new generation；
(8) fitness for calculating individual, judges whether the termination condition of algorithm meets, and algorithm terminates if meeting, and otherwise turns to walk
Suddenly (4), the number of iterations n.
4. it is adaptive that the algorithm enhancing that leapfrogs is shuffled in a kind of improvement of screw compressor fault diagnosis according to claim 2
Band-pass filtering method, it is characterised in that: the algorithm that leapfrogs of being shuffled in the step S2 based on improvement carries out adaptive band logical filter
The step of wave are as follows:
(1) variation range of tentatively specified bandpass filtering centre frequency and bandwidth；
(2) two-dimensional frog individual is determinedWherein fc: centre frequency fb: bandwidth), initialization population parameter,
Fitness function is established, determines that optimized individual fitness is optimization object function, establishes and stop design conditions, with optimization aim letter
Numerical value continuous five times be less than certain value when stop calculate；
(3) parameter optimization for carrying out self-adaptive band-pass filter to reconstruction signal based on improved SFLA algorithm is carried out, optimizing obtains
Centre frequency and bandwidth corresponding to optimal adaptation degree frog individual；
(4) optimal resonance and demodulation envelope spectrum corresponding to optimal centre frequency and bandwidth is obtained.
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